Experimental Flight Patterns Evaluation for a UAV-Based Air Pollutant Sensor
The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI M...
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Published in | Micromachines (Basel) Vol. 11; no. 8; p. 768 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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11.08.2020
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Abstract | The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community. |
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AbstractList | The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO
2
. Field tests were conducted in a 100 m
2
area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community. The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0-2.9m/s) to high (2.1-5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community.The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0-2.9m/s) to high (2.1-5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community. The use of drones in combination with remote sensors have displayed increasing interest over the last years due to its potential to automate monitoring processes. In this study, a novel approach of a small flying e-nose is proposed by assembling a set of AlphaSense electrochemical-sensors to a DJI Matrix 100 unmanned aerial vehicle (UAV). The system was tested on an outdoor field with a source of NO2. Field tests were conducted in a 100 m2 area on two dates with different wind speed levels varying from low (0.0–2.9m/s) to high (2.1–5.3m/s), two flight patterns zigzag and spiral and at three altitudes (3, 6 and 9 m). The objective of this study is to evaluate the sensors responsiveness and performance when subject to distinct flying conditions. A Wilcoxon rank-sum test showed significant difference between flight patterns only under High Wind conditions, with Spiral flights being slightly superior than Zigzag. With the aim of contributing to other studies in the same field, the data used in this analysis will be shared with the scientific community. |
Author | Valente, João Peters, Ruud J. B. Munniks, Sandra Araujo, João Otávio Kooistra, Lammert |
AuthorAffiliation | 1 Information Technology (INF), Wageningen University (WUR), Hollandseweg 1, 6706 KN Wageningen, The Netherlands; joaootavio.araujodasilva@wur.nl 2 Laboratory of Geo-Information Science and Remote Sensing, Wageningen University (WUR), Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands; lammert.kooistra@wur.nl 3 Wageningen Food Safety Research (WFSR), Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands; sandra.munniks@wur.nl (S.M.); ruudj.peters@wur.nl (R.J.B.P.) |
AuthorAffiliation_xml | – name: 1 Information Technology (INF), Wageningen University (WUR), Hollandseweg 1, 6706 KN Wageningen, The Netherlands; joaootavio.araujodasilva@wur.nl – name: 3 Wageningen Food Safety Research (WFSR), Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands; sandra.munniks@wur.nl (S.M.); ruudj.peters@wur.nl (R.J.B.P.) – name: 2 Laboratory of Geo-Information Science and Remote Sensing, Wageningen University (WUR), Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands; lammert.kooistra@wur.nl |
Author_xml | – sequence: 1 givenname: João Otávio orcidid: 0000-0002-7803-6703 surname: Araujo fullname: Araujo, João Otávio – sequence: 2 givenname: João orcidid: 0000-0002-6241-4124 surname: Valente fullname: Valente, João – sequence: 3 givenname: Lammert orcidid: 0000-0001-5549-5993 surname: Kooistra fullname: Kooistra, Lammert – sequence: 4 givenname: Sandra surname: Munniks fullname: Munniks, Sandra – sequence: 5 givenname: Ruud J. B. orcidid: 0000-0002-3948-5012 surname: Peters fullname: Peters, Ruud J. B. |
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SubjectTerms | Air pollution Chemical sensors COVID-19 Drone aircraft electrochemical sensors Electronic noses Field tests Flight gas sensing Ground stations Nitrogen dioxide Outdoor air quality Pollutants Public health remote sensing Remote sensors Satellites Sensors unmanned aerial vehicle Unmanned aerial vehicles Vehicles Wind speed |
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